Norwegian speech-to-text
GoodWe sent 30 Norwegian test clips through this API. Whisper Large v3 made 9.7% word errors, about one word in 10. $0.002 per audio minute, word timestamps, SRT and VTT subtitles, files up to 100 MB.
Measured accuracy
| Model | Word error rate | Character error rate | Clips with no errors | Empty transcripts | Speed |
|---|---|---|---|---|---|
Whisper Large v3openai/whisper-large-v3 | 9.7% | 3.0% | 7 of 30 | 0 of 30 | 0.06× real time |
Good. Usable with light review. Expect to correct a word here and there, mostly names and numbers. The clips are clean, single-speaker read speech, so treat this as a best case for your own recordings. How we measured, and all 83 languages.
What a transcript looks like
The clip with the median error rate from the test, reference text against what the API returned.
| Spoken | ASUS Eee-PC, som tidligere har blitt lansert over hele verden for sine kostnadseffektive og funksjonalistiske egenskaper, ble et hett tema under IT-måneden i Taipei 2007. |
| Whisper Large v3 | Asus iPC, som tidligere har blitt lansert over hele verden for sine kostnadseffektive og funksjonalistiske egenskaper, ble et hett tema under IT-måneden i Taipei 2007. |
Language detection
Send language=no. Of 10 clips sent without it, 9 were identified as Norwegian; the others were labelled English (1). When detection is wrong, Whisper writes the transcript in the language it guessed.
Quickstart
The request is the OpenAI transcription request with a different base URL. language is optional in the API but, as shown above, not optional in practice for Norwegian.
curl https://api.inferenceapis.com/v1/audio/transcriptions \
-H "Authorization: Bearer $INFERENCE_API_KEY" \
-F "model=openai/whisper-large-v3" \
-F "language=no" \
-F "file=@recording.mp3" \
-F "response_format=verbose_json"import os
from openai import OpenAI
client = OpenAI(base_url="https://api.inferenceapis.com/v1", api_key=os.environ["INFERENCE_API_KEY"])
with open("recording.mp3", "rb") as f:
r = client.audio.transcriptions.create(model="openai/whisper-large-v3", file=f, language="no", response_format="verbose_json")
print(r.text)Subtitles and timestamps
response_format=srt or vtt returns a subtitle file with cues of up to two lines, broken at sentence ends and pauses. verbose_json with timestamp_granularities[]=word returns a start and end time for every word if you want to build your own.
Cost
$0.002 per minute of audio on either model: $0.12 for an hour, $12 for a hundred hours. Duration is measured from the file you upload. There is no daily cap on minutes and no subscription; enough to run your own Norwegian test.
Notes for Norwegian
nocovers Bokmål. Nynorsk has its own code,nn.
FAQ
How accurate is Whisper for Norwegian?
On 30 clean read-speech clips it made 9.7% word errors, about one word in 10. Usable with light review. Expect to correct a word here and there, mostly names and numbers. Real recordings with noise, accents or several speakers will score worse.
Does language auto-detection work for Norwegian?
Mostly. Of 10 clips sent without a language, 9 were identified as Norwegian; the rest were labelled English (1). A wrong label means the transcript is written in the wrong language, so always send language=no.
Can it translate Norwegian speech to English?
The API returns Norwegian text. For English, transcribe first and translate the text with a chat model on the same key.